1. Field of the invention
This application is based upon and claims the benefit of priority from Japanese patent application No. 2006-229841, filed on Aug. 25, 2006, the disclosure of which is incorporated herein in its entirety by reference.
The present invention relates to a method of enhancing the texture of input images such as latent fingerprint images and palm print images including uneven density and background noises.
2. Related Art
Generally, a fingerprint configured of a plurality of streak pattern ridges has two main characteristics, which are eternal and unique, so it has been used as a means for crime investigation. In particular, fingerprint matching using latent fingerprints remained in a crime scene is an effective means for investigation. In recent years, a fingerprint matching system using computers has been introduced, and latent fingerprint matching is conducted in various police agencies.
However, a latent fingerprint image is often unclear since it has low quality and includes noises, so examination carried out by an examiner is not easy. Further, this causes a major disincentive for automation. FIG. 5 shows an example illustrating a digitalized latent fingerprint remained on a check. As shown in this example, the background densities in an area including background noises such as characters and patterns of the check and in an area without background noises of this kind are largely different. Thereby, the dynamic ranges of fingerprint ridges are extremely different.
FIG. 6 shows a profile of an image on a line L, which is an enlarged part near the background noise area boundary of the image shown in FIG. 5. In FIG. 6, it is found that the dynamic range of a fingerprint ridge on the background noise caused by a character is extremely smaller compared with the dynamic range of a fingerprint ridge in an area with no noise.
As the latent fingerprint shown in this example, even applying a related image enhancement method to an image in which the dynamic range of the target texture (fingerprint ridge) drastically changes in continuing areas, it is difficult to enhance only the target texture (fingerprint ridge) because the noise area boundary is enhanced.
Even applying the Adaptive Contrast Stretch or the Adaptive Histogram Equalization, which is a typical related image enhancement method, to the input image shown in FIG. 7A, an adverse effect that the background noise area boundary is enhanced too much as shown in FIG. 7B or the background noise area cannot be removed completely as shown in FIG. 7C is caused.
Various methods have been proposed to solve such a problem. For instance, Japanese Patent No. 3465226 (Patent Document 1) discloses an image density conversion method in which an input image is divided into areas based on the texture analysis, and smoothing level of the density histogram is determined for each area according to the size of the dynamic range, to thereby suppress elimination of useful information.
Further, U.S. Pat. No. 5,426,684, “Technique for finding the histogram region of interest for improved tone scale reproduction of digital radiographic images”, by Gaborski et al. (Patent Document 2) discloses an area dividing method using a neural network to determine appropriate reference areas.
However, in Patent Document 1, a certain appropriate reference area is provided around a target pixel and density conversion is performed based on the density histogram of the pixels included in the area. Therefore, an adverse effect that the background noise area is enhanced too much as shown in FIG. 7B or the background noise area cannot be removed completely as shown in FIG. 7C, described above, is not solved.
Further, even in the area dividing method using a neural network, it causes the same adverse effect that the background noise area cannot be removed completely, as the case of Patent Document 1.
Further, the density conversion method described in Patent Document 1 uses an area dividing method based on the texture analysis of an input image. Therefore, the method largely depends on the analysis accuracy, which causes such an adverse effect that the enhancement result will be deteriorated if area division is not processed as expected.
The problem caused in the related art is that even applying a related image enhancement method to an image in which the dynamic range of a target texture (fingerprint ridge) changes drastically and extremely in continuing areas or to an image in which a texture exists on areas where the densities are extremely different, it is difficult to enhance only the target texture because the noise area boundary is also enhanced.
Further, the related art also involves a shortcoming that if a reference area lies astride the boundary between a background noise area and an area without background noise near the background noise boundary, the density histogram is not always taken from the area where the target pixel belongs to, so the noise area boundary may be enhanced.
In view of the above, art capable of enhancing only a target texture, even in an image where the dynamic range of the target texture changes drastically and extremely in continuing areas, has been demanded.